{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# geektutu.com\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, datasets, models\n",
    "\n",
    "(train_x, train_y), (test_x, test_y) = datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# geektutu.com\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.subplots_adjust(hspace=0.1)\n",
    "for n in range(15):\n",
    "    plt.subplot(3, 5, n+1)\n",
    "    plt.imshow(train_x[n])\n",
    "    plt.axis('off')\n",
    "_ = plt.suptitle(\"geektutu.com CIFAR-10 Example\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x shape: (50000, 32, 32, 3) test_x shape: (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# geektutu.com\n",
    "train_x, test_x = train_x / 255.0, test_x / 255.0\n",
    "print('train_x shape:', train_x.shape, 'test_x shape:', test_x.shape)\n",
    "# (50000, 32, 32, 3), (10000, 32, 32, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卷积层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 4, 4, 64)          36928     \n",
      "=================================================================\n",
      "Total params: 56,320\n",
      "Trainable params: 56,320\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# geektutu.com\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 全连接层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 4, 4, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                65600     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 122,570\n",
      "Trainable params: 122,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# geektutu.com\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 编译训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0720 16:46:59.197520 140735530943360 deprecation.py:323] From /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples\n",
      "Epoch 1/5\n",
      "50000/50000 [==============================] - 20s 394us/sample - loss: 1.4972 - accuracy: 0.4585\n",
      "Epoch 2/5\n",
      "50000/50000 [==============================] - 19s 386us/sample - loss: 1.1277 - accuracy: 0.6025\n",
      "Epoch 3/5\n",
      "50000/50000 [==============================] - 20s 398us/sample - loss: 0.9836 - accuracy: 0.6541\n",
      "Epoch 4/5\n",
      "50000/50000 [==============================] - 20s 404us/sample - loss: 0.8808 - accuracy: 0.6917\n",
      "Epoch 5/5\n",
      "50000/50000 [==============================] - 20s 406us/sample - loss: 0.8040 - accuracy: 0.7179\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x138b07358>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_x, train_y, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 122us/sample - loss: 0.9077 - accuracy: 0.6830\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.683"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# geektutu.com\n",
    "test_loss, test_acc = model.evaluate(test_x, test_y)\n",
    "test_acc # 0.683"
   ]
  }
 ],
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